With AI Call Scoring, verify at scale how well your Sales representatives perform against your methodology and identify the most relevant coaching opportunities to boost your team’s performance.
In this article, you will learn how to write AI call scoring questions that will properly assess the calls of your teams.
Use AI to improve your questions
When setting up your AI call scoring template, you can benefit from AI assistance to properly frame your questions.
When you write your questions, you will see a “Refine question” button. If you click on it, the AI will suggest some rephrasing of your questions that will be more precise and built following prompting best practices. This will hence make the question better understandable by the AI and hence allow the AI to give relevant and precise answers to the questions you ask.
💡Tip: always ask the AI help to refine your question, this is the best way to ensure its quality upfront!
Follow our experts’ advices to write the questions
There are several best practices you can keep in mind when writing a question so that it is best understood by the AI:
Clarity and specificity
Formulate direct and specific questions so that the model can easily identify whether they were addressed during the call.
Example:
❌ "Did the salesperson ask questions about the prospect's situation?" (Too vague)
✅ "Did the salesperson ask what the prospect’s main current challenges are?" (Precise)
Avoid subjective and interpretative questions
Ask objective questions based solely on what is explicitly present in the transcript, not on interpretation or assumptions.
Avoid subjective terms like "effectively," "properly," "clearly", etc.
Example:
❌ "Did the salesperson effectively understand the prospect’s needs?" (Subjective)
✅ "Did the salesperson ask what the prospect's needs are?" (Objective)
Binary scoring
Each question should be answerable as true / partial / false, based on explicit content in the transcript.
Ensure clear, observable criteria are used.
Example:
❌ "Did the salesperson discuss in detail the prospect’s decision-making process?" (Vague, subjective)
✅ "Did the salesperson ask what the prospect’s decision criteria are?" (Clear, scorable)
Clarifications without biasing the model
Add clarifications when necessary to avoid ambiguity — but avoid including specific examples that could bias the model’s understanding.
If a question can be interpreted in multiple ways, clarify without narrowing the scope excessively.
The goal is to make the question clearer, not more specific or constrained.
Example:
❌ "Did the salesperson follow up?" (Vague)
✅ "Did the salesperson take any action to re-engage with the prospect after the conversation?" (Clarifies the intent — "follow up" — while staying neutral about how it was done)
⚠️ Note: Adding examples can bias the model — prefer clarifications over examples.
One idea per question
Each question should focus on one single idea to ensure clear and objective scoring.
Avoid composite questions (such as two questions in one).
Questions should not reference other questions from the same template (for independence and coherence).
Example:
❌ “Did the salesperson explain the product and address the prospect’s key objections ?"(Covers two separate actions — explaining and handling objections — which could lead to unclear or mixed scoring)
✅ "Did the salesperson explain the product ?"
✅ "Did the salesperson address the prospect’s key objections ?"
(Each question now focuses on one clear behavior and can be scored independently)
Adapt questions to the call type
Align questions with the specific context of the call (e.g., discovery, qualification, closing).
Focus on the salesperson’s actions
Questions must explicitly reference what the salesperson says, not the prospect.
Avoid formulations that create ambiguity about who is speaking.
Example:
❌ "Were goals mentioned during the call?" (Unclear)
✅ "Did the salesperson ask what the prospect’s goals are?" (Clear)
🎯 Test and validate your questions
Before finalizing, test your questions and templates on real calls to ensure they:
Are understandable by the model.
Lead to consistent and reliable answers.
Do not create ambiguity or rely on implicit knowledge.
💡 In a nutshell
Avoid overly complex formulations — keep it simple and model-friendly.
Remember that adding examples can bias the model’s answers — prefer rewording or clarifying the question itself when needed.